After following a tutorial on Recommender systems using Pyspark / Spark ML. I
decided to jump in with my own dataset. I am specifically trying to predict
video suggestions based on an implicit feature for the time a video was
watched. I wrote a generator to produce my dataset. I have a total of five
videos each 1200 seconds in length. I randomly selected which videos a user
watched and a random time between 0-1200. I generated 10k records. Weight is
the time watched feature. It looks a like this.
UserId,VideoId,Weight
0,1,645
0,2,870
0,3,1075
0,4,486
0,5,900
1,1,353
1,2,988
1,3,152
1,4,953
1,5,641
2,3,12
2,4,444
2,5,87
3,2,658
3,4,270
3,5,530
4,2,722
4,3,255
:
After reading the dataset. I convert all columns to Integer in place.
Describing Weight produces:
summary Weight
0 count 30136
1 mean 597.717945314574
2 stddev 346.475684454489
3 min 0
4 max 1200
Next, I standardized the weight column by:
df = dataset.select(mean('Weight').alias('mean_weight'),
stddev('Weight').alias('stddev_weight')).crossJoin(dataset).withColumn('weight_scaled',
(col('Weight') - col('mean_weight')) / col('stddev_weight'))
df.toPandas().head() shows:
mean_weight stddev_weight UserId VideoId Weight weight_scaled
0 597.717945 346.475684 0 1 645 0.136466
1 597.717945 346.475684 0 2 870 0.785862
2 597.717945 346.475684 0 3 1075 1.377534
3 597.717945 346.475684 0 4 486
-0.322441
4 597.717945 346.475684 0 5 900 0.872448
:
10 597.717945 346.475684 2 3 12 -1.690502
11 597.717945 346.475684 2 4 444 -0.443662
12 597.717945 346.475684 2 5 87
-1.474037
:
After splitting df 80 / 20 to get training / testing
I defined the ALS algo with:
als = ALS(maxIter=10, regParam=0.1, userCol='UserId', itemCol='VideoId',
implicitPrefs=True, ratingCol='weight_scaled', coldStartStrategy='drop')
and then
model = als.fit(trainingData)
Calling fit() is where I get the following error, I don't understand.
Py4JJavaError Traceback (most recent call last)
<ipython-input-12-20463d9db24b> in <module>
----> 1 model = als.fit(trainingData)
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\pyspark\ml\base.py in fit(self,
dataset, params)
130 return self.copy(params)._fit(dataset)
131 else:
--> 132 return self._fit(dataset)
133 else:
134 raise ValueError("Params must be either a param map or a
list/tuple of param maps, "
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\pyspark\ml\wrapper.py in
_fit(self, dataset)
286
287 def _fit(self, dataset):
--> 288 java_model = self._fit_java(dataset)
289 model = self._create_model(java_model)
290 return self._copyValues(model)
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\pyspark\ml\wrapper.py in
_fit_java(self, dataset)
283 """
284 self._transfer_params_to_java()
--> 285 return self._java_obj.fit(dataset._jdf)
286
287 def _fit(self, dataset):
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\lib\py4j-0.10.6-src.zip\py4j\java_gateway.py
in __call__(self, *args)
1158 answer = self.gateway_client.send_command(command)
1159 return_value = get_return_value(
-> 1160 answer, self.gateway_client, self.target_id, self.name)
1161
1162 for temp_arg in temp_args:
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\pyspark\sql\utils.py in
deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\lib\py4j-0.10.6-src.zip\py4j\protocol.py
in get_return_value(answer, gateway_client, target_id, name)
318 raise Py4JJavaError(
319 "An error occurred while calling {0}{1}{2}.\n".
--> 320 format(target_id, ".", name), value)
321 else:
322 raise Py4JError(
Py4JJavaError: An error occurred while calling o211.fit.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 5 in
stage 61.0 failed 1 times, most recent failure: Lost task 5.0 in stage 61.0
(TID 179, localhost, executor driver):
org.apache.spark.ml.optim.SingularMatrixException: LAPACK.dppsv returned 6
because A is not positive definite. Is A derived from a singular matrix (e.g.
collinear column values)?
at
org.apache.spark.mllib.linalg.CholeskyDecomposition$.checkReturnValue(CholeskyDecomposition.scala:65)
at
org.apache.spark.mllib.linalg.CholeskyDecomposition$.solve(CholeskyDecomposition.scala:41)
at
org.apache.spark.ml.recommendation.ALS$CholeskySolver.solve(ALS.scala:747)
at
org.apache.spark.ml.recommendation.ALS$$anonfun$org$apache$spark$ml$recommendation$ALS$$computeFactors$1.apply(ALS.scala:1699)
at
org.apache.spark.ml.recommendation.ALS$$anonfun$org$apache$spark$ml$recommendation$ALS$$computeFactors$1.apply(ALS.scala:1660)
at
org.apache.spark.rdd.PairRDDFunctions$$anonfun$mapValues$1$$anonfun$apply$37$$anonfun$apply$38.apply(PairRDDFunctions.scala:757)
at
org.apache.spark.rdd.PairRDDFunctions$$anonfun$mapValues$1$$anonfun$apply$37$$anonfun$apply$38.apply(PairRDDFunctions.scala:757)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at
org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:217)
at
org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1092)
at
org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1083)
at
org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:1018)
at
org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1083)
at
org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:809)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:335)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:286)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1599)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1587)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1586)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at
scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at
org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1586)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at scala.Option.foreach(Option.scala:257)
at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1820)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1769)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1758)
at
org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at
org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2027)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2124)
at
org.apache.spark.rdd.RDD$$anonfun$aggregate$1.apply(RDD.scala:1118)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.aggregate(RDD.scala:1111)
at
org.apache.spark.ml.recommendation.ALS$.computeYtY(ALS.scala:1711)
at
org.apache.spark.ml.recommendation.ALS$.org$apache$spark$ml$recommendation$ALS$$computeFactors(ALS.scala:1652)
at
org.apache.spark.ml.recommendation.ALS$$anonfun$train$4.apply(ALS.scala:972)
at
org.apache.spark.ml.recommendation.ALS$$anonfun$train$4.apply(ALS.scala:969)
at scala.collection.immutable.Range.foreach(Range.scala:160)
at org.apache.spark.ml.recommendation.ALS$.train(ALS.scala:969)
at org.apache.spark.ml.recommendation.ALS.fit(ALS.scala:674)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at
py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at
py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.ml.optim.SingularMatrixException: LAPACK.dppsv
returned 6 because A is not positive definite. Is A derived from a singular
matrix (e.g. collinear column values)?
at
org.apache.spark.mllib.linalg.CholeskyDecomposition$.checkReturnValue(CholeskyDecomposition.scala:65)
at
org.apache.spark.mllib.linalg.CholeskyDecomposition$.solve(CholeskyDecomposition.scala:41)
at
org.apache.spark.ml.recommendation.ALS$CholeskySolver.solve(ALS.scala:747)
at
org.apache.spark.ml.recommendation.ALS$$anonfun$org$apache$spark$ml$recommendation$ALS$$computeFactors$1.apply(ALS.scala:1699)
at
org.apache.spark.ml.recommendation.ALS$$anonfun$org$apache$spark$ml$recommendation$ALS$$computeFactors$1.apply(ALS.scala:1660)
at
org.apache.spark.rdd.PairRDDFunctions$$anonfun$mapValues$1$$anonfun$apply$37$$anonfun$apply$38.apply(PairRDDFunctions.scala:757)
at
org.apache.spark.rdd.PairRDDFunctions$$anonfun$mapValues$1$$anonfun$apply$37$$anonfun$apply$38.apply(PairRDDFunctions.scala:757)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at
org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:217)
at
org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1092)
at
org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1083)
at
org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:1018)
at
org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1083)
at
org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:809)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:335)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:286)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
Thanks in advance.
-S
